View source: R/draw_lm_cov_latent_cont.R
draw_lm_cov_latent_cont | R Documentation |
Function that draws samples from the LM model for continuous outcomes with individual covariates with specific parameters.
The function is no longer maintained. Please look at draw.LMlatentcont
function.
draw_lm_cov_latent_cont(X1, X2, param = "multilogit", Mu, Si, Be, Ga, fort = TRUE)
X1 |
desing matrix for the covariates on the initial probabilities (n x nc1) |
X2 |
desing matrix for the covariates on the transition probabilities (n x TT-1 x nc2) |
param |
type of parametrization for the transition probabilities ("multilogit" = standard multinomial logit for every row of the transition matrix, "difflogit" = multinomial logit based on the difference between two sets of parameters) |
Mu |
array of conditional means for the response variables (r x k) |
Si |
var-cov matrix common to all states (r x r) |
Be |
parameters affecting the logit for the initial probabilities |
Ga |
parametes affecting the logit for the transition probabilities |
fort |
to use fortran routine when possible (FALSE for not use fortran) |
Y |
array of continuous outcomes (n x TT x r) |
U |
matrix containing the sequence of latent states (n x TT) |
Francesco Bartolucci, Silvia Pandolfi, University of Perugia (IT), http://www.stat.unipg.it/bartolucci
## Not run:
# draw a sample for 1000 units, 10 response variable and 2 covariates
n <- 1000
TT <- 5
k <- 2
nc <- 2 #number of covariates
r <- 3 #number of response variables
fort <- TRUE
Mu <- matrix(c(-2,-2,0,0,2,2), r, k)
Si <- diag(r)
Ga <- matrix(c(-log(0.9/0.1),0.5,1), (nc+1)*(k-1), k)
Be <- array(c(0,0.5,1), (nc+1)*(k-1))
#Simulate covariates
X1 <- matrix(0, n, nc)
for(j in 1:nc) X1[,j] <- rnorm(n)
X2 <- array(0, c(n,TT-1,nc))
for (t in 1:(TT-1)) for(j in 1:nc){
if(t==1){
X2[,t,j] <- 0.5*X1[,j] + rnorm(n)
}else{
X2[,t,j] <- 0.5*X2[,t-1,j] + rnorm(n)
}
}
out <- draw_lm_cov_latent_cont(X1, X2, param = "multilogit", Mu, Si, Be, Ga, fort = fort)
## End(Not run)
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